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The Evolution and Convergence of Artificial Intelligence and Intelligent Robotics
- 1 Beijing University of Posts and Telecommunications, Beijing, China, 100080
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Abstract
Nowadays, AI's development, especially the emergence of large models, has given it a depth of thinking and generalization ability it never had before, and this ability can be applied across the board. As a field that has long intersected with AI technology, intelligent robots have shifted from past modes of thought and technical routes to today's large models. This paper comprehensively traces the technical evolution of intelligent robots from their inception to now, focusing on their path of integration with AI. Using literature research, comparative analysis, and trend analysis, combined with in - depth technical analysis, it identifies key moments in the evolution of intelligent robot technology and the internal logic of its integration with AI. The research indicates that the convergence of artificial intelligence and robotics has progressed from mere perception to cognitive capabilities, transitioning from specialized to general-purpose applications. Moving forward, this integration is expected to become increasingly prevalent as technological advancements steer towards broader applications.
Keywords
AI Robot, Multimodality, Artificial Intelligence, Reinforcement Learning, large language model
[1]. Lin, L. J. (1992). Reinforcement learning for robots using neural networks. Carnegie Mellon University.
[2]. Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., ... & Kavukcuoglu, K. (2016, June). Asynchronous methods for deep reinforcement learning. In International conference on machine learning (pp. 1928-1937). PmLR.
[3]. Gu, S., Holly, E., Lillicrap, T., & Levine, S. (2017, May). Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In 2017 IEEE international conference on robotics and automation (ICRA) (pp. 3389-3396). IEEE.
[4]. Mirowski, P., Pascanu, R., Viola, F., Soyer, H., Ballard, A. J., Banino, A., ... & Hadsell, R. (2016). Learning to navigate in complex environments. arXiv preprint arXiv:1611.03673.
[5]. Agrawal, P., Carreira, J., & Malik, J. (2015). Learning to see by moving. In Proceedings of the IEEE international conference on computer vision (pp. 37-45).
[6]. Peng, X. B., Andrychowicz, M., Zaremba, W., & Abbeel, P. (2018, May). Sim-to-real transfer of robotic control with dynamics randomization. In 2018 IEEE international conference on robotics and automation (ICRA) (pp. 3803-3810). IEEE.
[7]. Florence, P. R., Manuelli, L., & Tedrake, R. (2018). Dense object nets: Learning dense visual object descriptors by and for robotic manipulation. arXiv preprint arXiv:1806.08756.
[8]. Calandra, R., Owens, A., Jayaraman, D., Lin, J., Yuan, W., Malik, J., ... & Levine, S. (2018). More than a feeling: Learning to grasp and regrasp using vision and touch. IEEE Robotics and Automation Letters, 3(4), 3300-3307.
[9]. Mirchandani, S., Xia, F., Florence, P., Ichter, B., Driess, D., Arenas, M. G., ... & Zeng, A. (2023). Large language models as general pattern machines. arXiv preprint arXiv:2307.04721.
[10]. Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Dabis, J., Finn, C., ... & Zitkovich, B. (2022). Rt-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.06817.
[11]. Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., ... & Zitkovich, B. (2023). Rt-2: Vision-language-action models transfer web knowledge to robotic control. arXiv preprint arXiv:2307.15818.
[12]. Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., ... & Zeng, A. (2023, May). Code as policies: Language model programs for embodied control. In 2023 IEEE International Conference on Robotics and Automation (ICRA) (pp. 9493-9500). IEEE.
[13]. Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., ... & Zeng, A. (2022). Do as i can, not as i say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691.
[14]. Gupta, A., Savarese, S., Ganguli, S., & Fei-Fei, L. (2021). Embodied intelligence via learning and evolution. Nature communications, 12(1), 5721.
[15]. Driess, D., Xia, F., Sajjadi, M. S., Lynch, C., Chowdhery, A., Wahid, A., ... & Florence, P. (2023). Palm-e: An embodied multimodal language model.
Cite this article
Hong,M. (2025). The Evolution and Convergence of Artificial Intelligence and Intelligent Robotics. Applied and Computational Engineering,150,212-217.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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